Generic reciprocity based channel state information acquisition frameworks for advanced networks

ABSTRACT

Facilitating generic reciprocity-based channel state information acquisition frameworks for advanced networks (e.g., 4G, 5G, and beyond) is provided herein. Operations of a system can comprise determining first uplink channel state information for a first mobile device based on first downlink channel state information received from the first mobile device. The first mobile device can be from a group of mobile devices in a wireless communications network. The operations can also comprise training a model on a difference between the first downlink channel state information and the first uplink channel state information to a defined level of confidence. Further, the operations can comprise employing the model to determine, without receipt of second downlink channel state information from a second mobile device of the group of mobile devices, second uplink channel state information for the second mobile device.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 16/059,745 (now U.S. Pat. No.10,637,551), filed Aug. 9, 2018, and entitled “GENERIC RECIPROCITY BASEDCHANNEL STATE INFORMATION ACQUISITION FRAMEWORKS FOR ADVANCED NETWORKS,”the entirety of which application is hereby expressly incorporated byreference herein.

TECHNICAL FIELD

This disclosure relates generally to the field of mobile communicationand, more specifically, to channel state information acquisitionframeworks in wireless communication systems for advanced networks(e.g., 4G, 5G, and beyond).

BACKGROUND

To meet the huge demand for data centric applications, Third GenerationPartnership Project (3GPP) systems and systems that employ one or moreaspects of the specifications of the Fourth Generation (4G) standard forwireless communications will be extended to a Fifth Generation (5G)standard for wireless communications. Unique challenges exist to providelevels of service associated with forthcoming 5G, or other nextgeneration, standards for wireless communication.

BRIEF DESCRIPTION OF THE DRAWINGS

Various non-limiting embodiments are further described with reference tothe accompanying drawings in which:

FIG. 1 illustrates an example, non-limiting, communications system forfacilitating generic reciprocity-based channel state informationacquisition frameworks in accordance with one or more embodimentsdescribed herein;

FIG. 2 illustrates an example, non-limiting, communications system forcomparing unlink and downlink channel state information to facilitategeneric reciprocity-based channel state information acquisitionframeworks in accordance with one or more embodiments described herein;

FIG. 3 illustrates an example, non-limiting, communications system fordetermining channel state information for mobile devices in acommunications network while mitigating an amount of network traffic inaccordance with one or more embodiments described herein;

FIG. 4 illustrates an example, non-limiting, communications system forproviding generic reciprocity in accordance with one or more embodimentsdescribed herein;

FIG. 5 illustrates an example, non-limiting, communications system thatemploys automated learning to facilitate one or more of the disclosedaspects in accordance with one or more embodiments described herein;

FIG. 6 illustrates an example, non-limiting, method for genericreciprocity-based channel state information acquisition frameworks inaccordance with one or more embodiments described herein;

FIG. 7 illustrates an example, non-limiting, method for utilizingmachine learning to facilitate generic reciprocity-based channel stateinformation acquisition frameworks in accordance with one or moreembodiments described herein;

FIG. 8 illustrates an example, non-limiting, method for utilizingmachine learning to facilitate retaining a one-to-one mappingrelationship between uplink channel state information and reporteddownlink channel state information in accordance with one or moreembodiments described herein;

FIG. 9 illustrates an example block diagram of an example mobile handsetoperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein;and

FIG. 10 illustrates an example block diagram of an example computeroperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein.

DETAILED DESCRIPTION

One or more embodiments are now described more fully hereinafter withreference to the accompanying drawings in which example embodiments areshown. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various embodiments. However, the variousembodiments can be practiced without these specific details (and withoutapplying to any particular networked environment or standard).

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate genericreciprocity-based channel state information acquisition frameworks foradvanced networks. In one embodiment, described herein is a system thatcan comprise a processor and a memory that stores executableinstructions that, when executed by the processor, facilitateperformance of operations. The operations can comprise determining firstuplink channel state information for a first mobile device based onfirst downlink channel state information received from the first mobiledevice. The first mobile device can be from a group of mobile devices ina wireless communications network. The operations can also comprisetraining a model on a difference between the first downlink channelstate information and the first uplink channel state information to adefined level of confidence. Further, the operations can compriseemploying the model to determine, without receipt of second downlinkchannel state information from a second mobile device of the group ofmobile devices, second uplink channel state information for the secondmobile device.

In an example, the operations can comprise comparing the first downlinkchannel state information and the first uplink channel state informationduring an over-the-air calibration process. Further to this example, thefirst downlink channel state information can comprise a first reportedpre-coding matrix indicator. In addition, the first uplink channel stateinformation can comprise a first computed pre-coding matrix indicator.

According to some examples, the operations can comprise employing themodel to determine third uplink channel state information for a thirdmobile device. Further to these examples, a third downlink channel stateinformation is not received from the third mobile device.

In accordance with some implementations, training the model can comprisegenerating a data store that comprises a mapping relationship betweenthe first downlink channel state information and the first uplinkchannel state information.

According to some implementations, determining the first uplink channelstate information can comprise quantizing the first uplink channel stateinformation based on a codebook utilized by the first mobile device todetermine the first downlink channel state information.

In accordance with some implementations, determining first uplinkchannel state information can comprise estimating a spatial domainportion of the channel comprising a main signal transmitting angle. Inadditional, or alternative, implementations, determining first uplinkchannel state information can comprise estimating a spatial domainportion of the channel comprising a main signal receiving angle.

The operations can also comprise, according to some implementations,determining third uplink channel state information for the first mobiledevice based on third downlink channel state information received fromthe first mobile device. The operations can also comprise populating adata store with a one-to-one mapping relationship between the firstuplink channel state information and the first downlink channel stateinformation, and between the third uplink channel state information andthe third downlink channel state information.

Training the model can comprise utilizing machine learning forevaluation of a first difference between the first downlink channelstate information and the first uplink channel state informationaccording to various implementations.

In some implementations, the first downlink channel state informationcan comprise a reported pre-coding matrix indicator. The first uplinkchannel state information can comprise a determined pre-coding matrixindicator. Further, training the model can comprise comparing thereported pre-coding matrix indicator and the determined pre-codingmatrix indicator.

According to some implementations, the first downlink channel stateinformation can be received via an uplink feedback channel According toother implementations, the first downlink channel state information canbe received via a data traffic channel.

Another embodiment relates to a method that can comprise, based on firstreported downlink channel state information received from a first mobiledevice of a group of mobile devices in a wireless network, facilitating,by a network device of the wireless network and comprising a processor,a determination of first uplink channel state information for the firstmobile device. The method can also comprise populating, by the networkdevice, a data store that comprises respective differences between thefirst uplink channel state information and the first reported downlinkchannel state information. The network device can train a model on therespective differences in the data store. The model can be trained todetect the respective differences to a defined level of confidence.Further, the method can comprise utilizing, by the network device, themodel to determine second uplink channel state information for a secondmobile device of the group of mobile devices. Second reported downlinkchannel state information is not received from the second mobile device.

In an example, training the model can comprise utilizing machinelearning to train the model on the respective differences between thefirst uplink channel state information and the first reported downlinkchannel state information.

According to some implementations, prior to populating the data store,the method can comprise determining, by the network device, therespective differences during an over-the-air calibration process.

In some implementations, populating the data store can comprisefacilitating a one-to-one mapping relationship between the first uplinkchannel state information and the first reported downlink channel stateinformation based on the respective differences. According to someimplementations, utilizing the model to determine the second uplinkchannel state information for the second mobile device can comprisefacilitating a mitigation of an amount of network traffic within thewireless network as compared to the second mobile device providing thesecond reported downlink channel state information.

Yet another embodiment relates to a machine-readable storage medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations. The operations can comprisedetermining, for a first mobile device from a group of mobile devices ina wireless network, first uplink channel state information based onfirst downlink channel state information received from the first mobiledevice. The operations can also comprise populating a data store withinformation indicative of differences between the first downlink channelstate information and the first uplink channel state information andtraining a model on the information indicative of the differences to adefined confidence level. Further, the operations can comprisedetermining, for a second mobile device in the group of mobile devices,second uplink channel state information based on the model. Seconddownlink channel state information is not received from the secondmobile device.

According to some implementations, the operations can also compriseimplementing machine learning to train the model on the informationindicative of differences between the first downlink channel stateinformation and the first uplink channel state information. Further, theoperations can comprise utilizing the model for determining the seconduplink channel state information.

Populating the data store can comprise, according to someimplementations, storing the first uplink channel state information, thefirst downlink channel state information, and the information indicativeof differences in the data store as a one-to-one mapping relationship.

In accordance with some implementations, determining the first uplinkchannel state information can comprise quantizing the first uplinkchannel state information based on a codebook utilized by the firstmobile device to determine the first downlink channel state information.

To meet the huge demand for data centric applications, 3GPP is currentlydiscussing to extend the current 4G standards to 5G also called NewRadio (NR) access. Massive Multiple Input, Multiple Output (MIMO) is atechnology that enables NR to have better spectrum efficiency over anLTE system. To fully utilize the MIMO potentials, a large number ofantenna ports (e.g., up to around thirty-two ports) have been defined.Based on information theory, with the large number of antenna ports, alarge CSI feedback overhead is needed. An effective way to reducefeedback overhead is to utilize reciprocity in the radio channelReciprocity procedures have been extensively studied in Time DivisionDuplexing (TDD) systems. In Frequency Division Duplexing (FDD) systems,the downlink and uplink are using different frequency bands. Therefore,it cannot simply be assumed that the whole channel responses arereciprocal.

The conventional CSI feedback framework is based on feedback from theUser Equipment (UE) or mobile device. For example, type 2 CSI feedbackis based on a linear combination with sub-band phase and amplitudeadjustment for each selected beam. The feedback overhead can easilyreach over 100 bits per report. That imposes a huge challenge on theuplink feedback channel design. On the other hand, CRI based feedbackallows the UE to select one out of several precoded CSI-RS resource eachrepresents a beam. But how the base-station obtain the precoder for theCSI-RS resource is unknown. So, effectively, in an FDD system, there isno other choice than having a large number of feedback bits to obtainthe fine granular CSI at the gNB (e.g., network device) side.

The various aspects provided herein relate to facilitating genericreciprocity-based channel state information acquisition frameworks.Provided is an FDD reciprocity CSI process procedure. According to someaspects, a database can be constructed to track the downlink and uplinkCSI difference. The database construction can be based on anover-the-air calibration process, which can compare the reported CSI anduplink CSI.

Referring initially to FIG. 1, illustrated is an example, non-limiting,communications system 100 for facilitating generic reciprocity-basedchannel state information acquisition frameworks in accordance with oneor more embodiments described herein.

The communications system 100 can comprise a first mobile device 102, atleast a second mobile device 104, and a network device 106. The networkdevice 106 can be included in a group of network devices of a wirelessnetwork. Although only two mobile devices and a single network deviceare shown and described, the various aspects are not limited to thisimplementation. Instead, multiple communication devices and/or multiplenetwork devices can be included in a communications system.

The first mobile device 102 can include an estimation component 108, areport component 110, a transmitter/receiver 112, at least one memory114, at least one processor 116, and at least one data store 118. Thenetwork device 106 can include an analysis component 120, a trainingcomponent 122, determination component 124, a communications component126, at least one memory 128, at least one processor 130, and at leastone data store 132. Although not illustrated or described for purposesof simplicity, the second mobile device 104 can include similarcomponents and functionality as the first mobile device 102.

The estimation component 108 can determine channel state information(CSI) of a downlink channel. The CSI can be included in a report (e.g.,a CSI report) generated by the report component 110. Information thatcan be included in the report can include, but are not limited to, oneor more CSI Resource Indicators (CRIs), one or more Rank Indicators(RIs), one or more Precoding Matrix Indicators (PMI), one or more LIs,and/or one or more Channel Quality Indicators (CQIs). The CSI report canbe transmitted to the network device 106 as first downlink CSI via thetransmitter/receiver 112.

The analysis component 120 can be configured to determine first uplinkchannel state information for the first mobile device 102 based on firstdownlink channel state information received from the first mobile device102. The first mobile device 102 can be from a group of mobile devicesin a wireless communications network. For example, the group of mobiledevices can include the first mobile device 102, the second mobiledevice 104, and other mobile devices (not illustrated). Thus, in theexample illustrated in FIG. 1, the first mobile device 102 can beselected to provide the downlink channel state information. However,according to some implementations, more than one mobile device, but lessthan all mobile devices, can be selected to provide the downlink channelstate information. For example, two or more mobile devices can beselected to provide the downlink channel state information.

According to some implementations, the analysis component 120 candetermine first uplink channel state information based on estimating aspatial domain portion of the channel comprising a main signaltransmitting angle or beam. In additional and/or alternativeimplementations, the analysis component 120 can determine first uplinkchannel state information based on estimating a spatial domain portionof the channel comprising a main signal receiving angle and/or beam. Forexample, further to these implementations, the CSI can be in the formatan arrival angle and/or an arrival beam.

The training component 122 can be configured to train a model 134 on adifference between the first downlink channel state information and thefirst uplink channel state information. The training component 122 cantrain the model to a defined level of confidence. The confidence levelcan be defined based on an acceptable amount of inaccuracy associatedwith uplink channel state information for mobile devices within thecommunications network.

The determination component 124 can employ the model 134 to determinesecond uplink channel state information for the second mobile device104. The determination by the determination component 124 can beperformed without receipt of second downlink channel state informationfrom the second mobile device 104. According to implementations wheretwo or more mobile devices are selected to provide the downlink channelstate information, the model 134 can be utilized to determine uplinkchannel state information for mobile devices in the communicationsnetwork, other than the two or more mobile devices.

The transmitter/receiver 112 (and/or the communications component 126)can be configured to transmit to, and/or receive data from, the networkdevice 106 (or the first mobile device 102), other network devices,and/or other communication devices (e.g., the second mobile device 104).Through the transmitter/receiver 112 (and/or the communicationscomponent 126), the first mobile device 102 (and/or the network device106) can concurrently transmit and receive data, can transmit andreceive data at different times, or combinations thereof. According tosome implementations, the transmitter/receiver 112 (and/or thecommunications component 126) can facilitate communications between anidentified entity associated with the first mobile device 102 (e.g., anowner of the first mobile device 102, a user of the first mobile device102, and so on). Further, the transmitter/receiver 112 (and/or thecommunications component 126) can be configured to receive, from thenetwork device 106 or other network devices, multimedia content asdiscussed herein.

The at least one memory 114 can be operatively connected to the at leastone processor 116. Further, the at least one memory 128 can beoperatively connected to the at least one processor 130. The memories(e.g., the at least one memory 114, the at least one memory 128) canstore executable instructions that, when executed by the processors(e.g., the at least one processor 116, the at least one processor 130)can facilitate performance of operations. Further, the processors can beutilized to execute computer executable components stored in thememories.

For example, the memories can store protocols associated withfacilitating generic feedback to enable reciprocity and over the aircalibration as discussed herein. Further, the memories can facilitateaction to control communication between the first mobile device 102 andthe network device 106 such that the communications system 100 canemploy stored protocols and/or algorithms to achieve improvedcommunications in a wireless network as described herein.

It should be appreciated that data stores (e.g., memories) componentsdescribed herein can be either volatile memory or nonvolatile memory, orcan include both volatile and nonvolatile memory. By way of example andnot limitation, nonvolatile memory can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory caninclude random access memory (RAM), which acts as external cache memory.By way of example and not limitation, RAM is available in many formssuch as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM(SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM),Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). Memory of thedisclosed aspects are intended to comprise, without being limited to,these and other suitable types of memory.

Further, the term network device (e.g., network node, network nodedevice) is used herein to refer to any type of network node servingcommunication devices and/or connected to other network nodes, networkelements, or another network node from which the communication devicescan receive a radio signal. In cellular radio access networks (e.g.,universal mobile telecommunications system (UMTS) networks), networknodes can be referred to as base transceiver stations (BTS), radio basestation, radio network nodes, base stations, NodeB, eNodeB (e.g.,evolved NodeB), and so on. In 5G terminology, the network nodes can bereferred to as gNodeB (e.g., gNB) devices. Network nodes can alsocomprise multiple antennas for performing various transmissionoperations (e.g., MIMO operations). A network node can comprise acabinet and other protected enclosures, an antenna mast, and actualantennas. Network nodes can serve several cells, also called sectors,depending on the configuration and type of antenna. Examples of networknodes (e.g., network device 106) can include but are not limited to:NodeB devices, base station (BS) devices, access point (AP) devices, andradio access network (RAN) devices. The network nodes can also includemulti-standard radio (MSR) radio node devices, comprising: an MSR BS, aneNode B, a network controller, a radio network controller (RNC), a basestation controller (BSC), a relay, a donor node controlling relay, abase transceiver station (BTS), a transmission point, a transmissionnode, a Remote Radio Unit (RRU), a Remote Radio Head (RRH), nodes indistributed antenna system (DAS), and the like.

FIG. 2 illustrates an example, non-limiting, communications system 200for comparing unlink and downlink channel state information tofacilitate generic reciprocity-based channel state informationacquisition frameworks in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity. Thecommunications system 200 can comprise one or more of the componentsand/or functionality of the communications system 100, and vice versa.

CSI acquisition is key component of MIMO operation in any wirelesssystem. Especially in LTE FD-MIMO and NR, the number of antenna ports issignificant larger. As a result, the CSI feedback overhead alsoincreases significantly. Conventionally, the channel reciprocity can beutilized to save CSI feedback overhead in a TDD system. Discussed hereinis a generic reciprocity-based CSI acquisition scheme which isapplicable to both a TDD system and an FDD system.

From a downlink RS, the first mobile device 102 can estimate the CSI(e.g., via the estimation component 108) and report the CSI to thenetwork node device (e.g., the network device 106) using an uplinkfeedback channel or a data channel (e.g., via the report component 110and the transmitter/receiver 112). The CSI can comprise one or moreCRIs, one or more RIs, one or more CQIs, and/or one or more LIs.

The gNB (e.g., the network device 106) can estimate an uplink channelfor the same mobile device (e.g., the first mobile device 102) based onuplink SRS (e.g., via the analysis component 120). For example, theestimated uplink channel can be quantized according to a codebook 202.Assuming the uplink channel is quantized as CSI-UL. According to someembodiments, the network device 106 can use a codebook 204 used by themobile device to obtain PMI and the network device 106 can derive a PMIbased on the uplink channel estimate. Thus, the codebook 202 and anothercodebook 204 utilized by the first mobile device 102 can be a samecodebook. In accordance with other embodiments, the network device 106can use the Singular Value Decomposition (SVD) of the channel estimateand can obtain the PMI which is closest to the SVD of the uplink channelmatrix.

A comparison component 206 can compare the reported PMI as part of CSIfrom the first mobile device 102 and the computed PMI from CSI-UL.Further, the network device 106 (e.g., the gNB) can generate a database(e.g., the at least one data store 118 or another data store) with amapping relationship between uplink CSI and downlink CSI.

FIG. 3 illustrates an example, non-limiting, communications system 300for determining channel state information for mobile devices in acommunications network while mitigating an amount of network traffic inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity. The communications system 300 cancomprise one or more of the components and/or functionality of thecommunications system 100, the communications system 200, and viceversa.

As mentioned, advanced networks can have a larger number of antennaelements inside one antenna. The channel itself can become a very largemetric (M×N) due to the number of transmit antennas (M) and the numberof receive antennas (N). If the transmit antennas become very large,then the metric itself scales proportionally (e.g., becomes very large).

Traditionally, there is a reciprocity-based framework where it isassumed that the uplink channel and the downlink channel are reciprocal.In this framework, the base station (e.g., the network device 106)directly estimates the uplink channel, then assumes it is the sameestimation for the downlink channel. However, the issue is that thisutilizes a difficult calculation process, referred to as a reciprocitycalibration, in which the phase and transmission chain are the same forevery antenna. However, it is expensive to establish a separate specialantenna element and to perform this calibration procedure. Further, thecalibration has to be performed on the fly because the phase of eachantenna branch or each antenna transmission or receiving chain canchange due to various conditions. For example, a calculation might needto be every 100 milliseconds, which can become expensive andcomputationally intensive.

Accordingly, the various aspects provided herein do not rely on thereciprocity calibration. Instead a certain amount of phase offsetbetween the transmission chain and the receiving chain is accepted sinceit cannot be assumed that the channel received in the uplink is the sameas the downlink. Instead, there will be some unknown offset, which willbe transferred to the downlink channel Thus, instead of the traditioncalibration process, which performs an estimation and transmits theuplink channel into a downlink channel, the disclosed aspects can beconfigured to build a database 302. According to some implementations,the database 302 can be included, at least partially, in the at leastone data store 132. A one-to-one relationship between the uplinkreceived channel, which can be quantified into the codebook 202, andthen migrated to a downlink transmission precoder.

For example, the first mobile device 102 can receive bits and monitorinformation. Then the network device 106 can estimate the uplink channelat about the same time the first mobile device 102 is performing themeasurement, based on uplink reference signals. Thereafter, the uplinkestimated channel can be quantified and compared to the mobile devicemeasured channel (e.g., via the analysis component 120). This can beused as input to a machine learning algorithm and a relationshipcomponent 304 can build a data mapping between the downlink channel andthe uplink channel.

According to the various aspects discussed herein, one or two mobiledevices (e.g., the first mobile device and the second mobile device 104)report respective downlink channel measurements. For example, the firstmobile device 102 can report a first downlink channel measured at thefirst mobile device 102, and the second mobile device 104 can report asecond downlink channel measured at the second mobile device 104. Themeasurements reported by the first mobile device 102 and the secondmobile device 104 are utilized by the training component 122 to trainthe model 134. Upon or after training the model 134, the information inthe database 302 can be applied to the remainder of the mobile devices(e.g., the other mobile devices 306) in the communications network. Incontrast, in a conventional framework, every mobile device providesfeedback, which increases an amount of network traffic.

According to some implementations, a format of CSI and CSI-UL can becovariance matrix, effective downlink precoder, and so on. In anexample, the database generation can be based on machine learning (e.g.a Wiener filter can help to find the mapping relationship betweendownlink and uplink CSI).

To reduce the complexity, in some embodiments, the network device 106can use the database to generate downlink precoder based on uplinkchannel estimation for the other mobile devices 306. In otherembodiments the network device 106 can estimate the uplink channel andcompare the reported PMI and generate the database for individual mobileand uses its own data base (e.g., the database 302).

By way of example and not limitation, an application example of thisframework can be GoB beamforming. For example, a CSI format can beprecoder (PMI feedback) based on a certain codebook. The uplink CSI canbe the effective downlink precoder. The uplink CSI can be based onreceived uplink SRS, and the network device 106 can estimate thecovariance matrix of uplink channel. Further, the network device 106 canquantize the precoder according to the same codebook used for mobiledevice's PMI report. Thereafter, the index of selected DFT beam can bederived from the downlink PMI and the uplink PMI. The database cancapture the mapping between the beam indexes of uplink CSI and downlinkCSI. As the selected DFT beam is associated with angle of signalarrival, this database essentially captures the offset relationshipbetween the downlink transmission and uplink reception. For other mobiledevices, the network device 106 can estimate uplink channel based onSRS, then quantize the channel based on the codebook. Then the networkdevice 106 can use the DFT beam index to look up the database to findthe corresponding downlink DFT beam index. Based on this, the networkdevice 106 can obtain the reciprocity-based CSI.

FIG. 4 illustrates an example, non-limiting, communications system 400for providing generic reciprocity in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity. The communications system 400 can comprise one or more of thecomponents and/or functionality of the communications system 100, thecommunications system 200, the communications system 300, and viceversa.

Provided herein is a reciprocity CSI process procedure. According tovarious aspects, a database can be constructed to track the downlink anduplink CSI difference. The database construction can be based on anover-the-air calibration process which compare the reported CSI anduplink CSI.

Current CSI feedback framework is based on mobile device feedback. Forexample, type-2 CSI feedback is based on linear combination withsub-band phase and amplitude adjustment for each selected beam. Thefeedback overhead can easily reach over 100 bits for each report. Thatcan impose huge challenges on the uplink feedback channel design. On theother hand, CRI based feedback allows mobile devices to select one outof several precoded CSI-RS resources, where each represents a beam. Howthe base-station obtains the precoder for those CSI-RS resources isunknown. So, effectively, in an FDD system, there is no other choicethan having a large number of feedback bits to obtain the fine granularCSI at gNB side.

The framework provided herein is a generic procedure to obtain thedownlink uplink calibration. For example, based on one mobile device'sCSI feedback, the network can estimate the CSI offset between uplink anddownlink, then apply the offset to other mobile devices. In practice,the downlink CSI and uplink CSI can be quantized based on the sameformat. The offset between downlink CSI and uplink CSI can be used toconstruct the database. Also, multiple mobile devices can participate inthe construction of this database which can help to reduce theimpairment of the database construction. Significant gains can beobtained at the link and system level as the complete channelinformation is known at the transmitter even for FDD systems. Thenetwork can configure the CSI reporting less frequent thereby reducingthe feedback channel overhead from the mobile device. Hence theseresources can be used for data traffic channel, thereby increasing thecapacity for uplink.

The analysis component 120 can determine, for a first mobile device(e.g., the first mobile device 102) selected from a group of mobiledevices (e.g., the second mobile device 104, the other mobile devices306) in a wireless network, first uplink channel state information basedon first downlink channel state information received from the firstmobile device. In some implementations, a quantizer component 402 canquantize the first uplink channel state information based on a codebook(e.g., the codebook 204) utilized by the first mobile device todetermine the first downlink channel state information.

A data store (e.g., the at least one data store 132, the database 302)can be populated, via a retention component 404, with informationindicative of differences between the first downlink channel stateinformation and the first uplink channel state information. For example,the retention component 404 can store the first uplink channel stateinformation, the first downlink channel state information, and theinformation indicative of differences in the data store as a one-to-onemapping relationship.

The training component 122 can train the model 134 on the informationindicative of the differences to a defined confidence level. Accordingto some implementations, machine learning can be implemented to trainthe model on the information indicative of differences between the firstdownlink channel state information and the first uplink channel stateinformation. Further to these implementations, the model can be utilizedto determine the second uplink channel state information.

In some aspects, the communications system 400 can perform a set ofmachine learning computations associated with training a model. Forexample, the communications system 400 can perform a set of clusteringmachine learning computations, a set of logistic regression machinelearning computations, a set of decision tree machine learningcomputations, a set of random forest machine learning computations, aset of regression tree machine learning computations, a set of leastsquare machine learning computations, a set of instance-based machinelearning computations, a set of regression machine learningcomputations, a set of support vector regression machine learningcomputations, a set of k-means machine learning computations, a set ofspectral clustering machine learning computations, a set of rulelearning machine learning computations, a set of Bayesian machinelearning computations, a set of deep Boltzmann machine computations, aset of deep belief network computations, and/or a set of differentmachine learning computations to train the model. Further detailsrelated to the machine learning aspects with be discussed below withrespect to FIG. 5.

The determination component 124 can determine, for a second mobiledevice (e.g., the second mobile device 104) in the group of mobiledevices, second uplink channel state information based on the model.Downlink channel state information is not received from the secondmobile device. Accordingly, network traffic can be reduced and/ormitigated in accordance with the various aspects provided herein.

FIG. 5 illustrates an example, non-limiting, communications system 500that employs automated learning to facilitate one or more of thedisclosed aspects in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Thecommunications system 500 can comprise one or more of the componentsand/or functionality of the communications system 100, thecommunications system 200, the communications system 300, thecommunications system 400, and vice versa.

As illustrated, the communications system 500 can comprise a machinelearning and reasoning component 502 that can be utilized to automateone or more of the disclosed aspects. The machine learning and reasoningcomponent 502 can employ automated learning and reasoning procedures(e.g., the use of explicitly and/or implicitly trained statisticalclassifiers) in connection with performing inference and/orprobabilistic determinations and/or statistical-based determinations inaccordance with one or more aspects described herein.

For example, the machine learning and reasoning component 502 can employprinciples of probabilistic and decision theoretic inference.Additionally, or alternatively, the machine learning and reasoningcomponent 502 can rely on predictive models constructed using machinelearning and/or automated learning procedures. Logic-centric inferencecan also be employed separately or in conjunction with probabilisticmethods.

The machine learning and reasoning component 502 can infer which uplinkchannel state information should be selected for use with a particularreported downlink channel state information, which uplink channel stateinformation should be selected for mobile devices from which downlinkchannel state information is not received, and so on. Such inference canbe performed by the machine learning and reasoning component 502 byobtaining knowledge about the reported channel state information,associated environmental conditions, associated channel conditions,associated mobile device information, and other information that wouldbe useful by the network device 106 to perform over-the-air calibrationof the first mobile device 102. The inference can be performed at aboutthe same time as reported downlink channel state information is receivedat the network device 106 (e.g., via the transmitter/receiver 112).

Based on this knowledge, the machine learning and reasoning component502 can make an inference based on which uplink channel stateinformation should be defined for one or more mobile devices (e.g., thefirst mobile device 102, the second mobile device 104, and/or othermobile devices).

As used herein, the term “inference” refers generally to the process ofreasoning about or inferring states of a system, a component, a module,an environment, and/or devices from a set of observations as capturedthrough events, reports, data and/or through other forms ofcommunication. Inference can be employed to identify a specific contextor information, or can generate a probability distribution over states,for example. The inference can be probabilistic. For example,computation of a probability distribution over states of interest basedon a consideration of data and/or conditions. The inference can alsorefer to techniques employed for composing higher-level information froma set of conditions and/or data. Such inference can result in theconstruction of new conditions and/or actions from a set of observedconditions and/or stored conditions data, whether or not the conditionsare correlated in close temporal proximity, and whether the conditionsand/or data come from one or several conditions and/or data sources.Various classification procedures and/or systems (e.g., support vectormachines, neural networks, logic-centric production systems, Bayesianbelief networks, fuzzy logic, data fusion engines, and so on) can beemployed in connection with performing automatic and/or inferred actionin connection with the disclosed aspects.

The various aspects (e.g., in connection with the selection of uplinkchannel state information, evaluation of network conditions and/ordevice conditions, and so forth) can employ various artificialintelligence-based procedures for carrying out various aspects thereof.

A classifier is a function that maps an input attribute vector, x=(x1,x2, x3, x4, xn), to a confidence that the input belongs to a class. Inother words, f(x)=confidence(class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to provide a prognosis and/or inferone or more actions that should be employed to identify differencesbetween reported and determined channel state information and whichchannel state information should be selected from a group of channelstate information at a particular moment in time (e.g., at about thesame time as reported channel state information is received from thenetwork device 106).

A Support Vector Machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that can be similar, but notnecessarily identical to training data. Other directed and undirectedmodel classification approaches (e.g., naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models) providing different patterns of independence canbe employed. Classification as used herein, can be inclusive ofstatistical regression that is utilized to develop models of priority.

One or more aspects can employ classifiers that are explicitly trained(e.g., through a generic training data) as well as classifiers that areimplicitly trained (e.g., by observing channel conditions, by receivingextrinsic information about what is needed by the network device 106 toperform over-the-air calibration, and so on). For example, SVM's can beconfigured through a learning or training phase within a classifierconstructor and feature selection module. Thus, a classifier(s) can beused to automatically learn and perform a number of functions,including, but not limited to, determining according to a predeterminedcriterion when to select a particular channel state information, when toexclude information, and so forth. The criteria can include, but is notlimited to, similar conditions, historical information, and so forth.

Additionally, or alternatively, an implementation procedure (e.g., arule, a policy, and so on) can be applied to control and/or regulateinformation in order to mitigate an amount of unnecessary overhead, andso forth. In some implementations, based upon a predefined criterion,the rules-based implementation can automatically and/or dynamicallycalibrate one or more mobile devices. In response thereto, therule-based implementation can automatically interpret and carry outfunctions associated with the conditions by employing a predefinedand/or programmed rule(s) based upon any desired criteria.

Advantages of the disclosed aspects include, but are not limited to,providing a generic reciprocity-based CSI acquisition framework thatcomprises a generic procedure to obtain the downlink uplink calibration.Based on one mobile device's CSI feedback, the network device canestimate the CSI offset between uplink and downlink, then apply theoffset to other mobile devices. In practice, the downlink CSI and uplinkCSI can be quantized based on the same format. The offset betweendownlink CSI and uplink CSI can be used to construct the database. Also,multiple mobile devices can participate to the construction of thisdatabase, which can help to reduce and/or mitigate the impairment of thedatabase construction. In addition, significant gains can be obtained atthe link and system level as the complete channel information is knownat the transmitter, even for FDD systems. Further, the network devicecan configure the CSI reporting less frequently thereby reducing thefeedback channel overhead from the mobile device. Hence these resourcescan be used for data traffic channel, thereby increasing the capacityfor uplink resources.

Methods that can be implemented in accordance with the disclosed subjectmatter, will be better appreciated with reference to the following flowcharts. While, for purposes of simplicity of explanation, the methodsare shown and described as a series of blocks, it is to be understoodand appreciated that the disclosed aspects are not limited by the numberor order of blocks, as some blocks can occur in different orders and/orat substantially the same time with other blocks from what is depictedand described herein. Moreover, not all illustrated blocks can berequired to implement the disclosed methods. It is to be appreciatedthat the functionality associated with the blocks can be implemented bysoftware, hardware, a combination thereof, or any other suitable means(e.g. device, system, process, component, and so forth). Additionally,it should be further appreciated that the disclosed methods are capableof being stored on an article of manufacture to facilitate transportingand transferring such methods to various devices. Those skilled in theart will understand and appreciate that the methods could alternativelybe represented as a series of interrelated states or events, such as ina state diagram.

FIG. 6 illustrates an example, non-limiting, method 600 for genericreciprocity-based channel state information acquisition frameworks inaccordance with one or more embodiments described herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity. The method 600 can be implementedby a network device of a wireless network, the network device comprisinga processor. Alternatively, or additionally, a machine-readable storagemedium can comprise executable instructions that, when executed by aprocessor, facilitate performance of operations for the method 600.

The method 600 starts, at 602, with facilitating a determination offirst uplink channel state information for a first mobile device (e.g.,via the analysis component 120). The determination can be based on firstreported downlink channel state information received from a first mobiledevice of a group of mobile devices in a wireless network. A data storecan be populated, at 604, with respective differences between the firstuplink channel state information and the first reported downlink channelstate information (e.g., via the retention component 404).

In accordance with some implementations, determining first uplinkchannel state information can comprise estimating a spatial domainportion of the channel comprising a main signal transmitting angle. Inadditional, or alternative, implementations, determining first uplinkchannel state information can comprise estimating a spatial domainportion of the channel comprising a main signal receiving angle.

Further, at 606, a model can be trained on the respective differences inthe data store (e.g., via the training component 122). According to someimplementations, the model can be trained to detect the respectivedifferences to a defined level of confidence.

The model can be used, at 608, to determine second uplink channel stateinformation for a second mobile device of the group of mobile devices(e.g., via the determination component 124). Second reported downlinkchannel state information is not received from the second mobile device.

FIG. 7 illustrates an example, non-limiting, method 700 for utilizingmachine learning to facilitate generic reciprocity-based channel stateinformation acquisition frameworks in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity. The method 700 can be implemented by a network device of awireless network, the network device comprising a processor.Alternatively, or additionally, a machine-readable storage medium cancomprise executable instructions that, when executed by a processor,facilitate performance of operations for the method 700.

A first downlink channel state information can be received from a firstmobile device in a wireless communications network, at 702 (e.g., viathe communications component 126). The first mobile device can be amobile device selected from a group of mobile devices in the wirelesscommunications network. Further, downlink CSI information is notreceived from the other devices in the wireless communications network.

Based on the first reported downlink channel state information, at 704,a determination of first uplink channel state information for the firstmobile device can be performed (e.g., via the analysis component 120). Adata store can be populated, at 706, with respective differences betweenthe first uplink channel state information and the first reporteddownlink channel state information (e.g., via the retention component404).

At 708, machine learning can be utilized to train the model on therespective differences between the first uplink channel stateinformation and the first reported downlink channel state information(e.g., via the training component 122 or the machine learning andreasoning component 502). For example, the model can be trained on therespective differences in the data store, wherein the model is trainedto detect the respective differences to a defined level of confidence.

Further, the model can be utilized, at 710 to determine at least asecond uplink channel state information for at least a second mobiledevice of the group of mobile devices (e.g., via the determinationcomponent 124). At least the second uplink channel state information canbe determined without receipt of a second reported downlink channelstate information from the second mobile device.

FIG. 8 illustrates an example, non-limiting, method 800 for utilizingmachine learning to facilitate retaining a one-to-one mappingrelationship between uplink channel state information and reporteddownlink channel state information in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity. The method 800 can be implemented by a network device of awireless network, the network device comprising a processor.Alternatively, or additionally, a machine-readable storage medium cancomprise executable instructions that, when executed by a processor,facilitate performance of operations for the method 800.

Based on a first reported downlink channel state information receivedfrom a first mobile device of a group of mobile devices in a wirelessnetwork, at 804, the method 800 can facilitate a determination of firstuplink channel state information for the first mobile device (e.g., viathe analysis component 120). Differences between the first uplinkchannel state information and the reported downlink channel stateinformation can be determined at 804 (e.g., via the determinationcomponent 124). According to some implementations, the differences canbe determined during an over-the-air calibration process.

The differences can be retained in a data store, at 806 (e.g., via theretention component 404). For example, the data store can be populatedwith the respective differences between the first uplink channel stateinformation and the first reported downlink channel state information.According to some implementations, retaining the differences in the datastore can comprise facilitating a mitigation of an amount of networktraffic within the wireless network as compared to the second mobiledevice providing the second reported downlink channel state information.

A model can be trained on the differences, at 808 (e.g., via thetraining component 122). For example, the model can be trained to detectthe differences to a defined confidence level. The model can be used, at810, to determine uplink channel state information for other mobiledevices of the group of mobile devices that do not report downlinkchannel state information (e.g., via the determination component 124).Determining the uplink channel state information for the other mobiledevices can facilitate a mitigation of an amount of network trafficwithin the wireless network as compared to the other mobile devicesreporting downlink channel state information.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate genericreciprocity-based channel state information acquisition frameworks foradvanced networks. Facilitating generic reciprocity-based channel stateinformation acquisition frameworks for advanced networks can beimplemented in connection with any type of device with a connection tothe communications network (e.g., a mobile handset, a computer, ahandheld device, etc.) any Internet of things (IoT) device (e.g.,toaster, coffee maker, blinds, music players, speakers, etc.), and/orany connected vehicles (cars, airplanes, space rockets, and/or other atleast partially automated vehicles (e.g., drones)). In some embodiments,the non-limiting term User Equipment (UE) is used. It can refer to anytype of wireless device that communicates with a radio network node in acellular or mobile communication system. Examples of UE are targetdevice, device to device (D2D) UE, machine type UE or UE capable ofmachine to machine (M2M) communication, PDA, Tablet, mobile terminals,smart phone, Laptop Embedded Equipped (LEE), laptop mounted equipment(LME), USB dongles etc. Note that the terms element, elements andantenna ports can be interchangeably used but carry the same meaning inthis disclosure. The embodiments are applicable to single carrier aswell as to Multi-Carrier (MC) or Carrier Aggregation (CA) operation ofthe UE. The term Carrier Aggregation (CA) is also called (e.g.,interchangeably called) “multi-carrier system,” “multi-cell operation,”“multi-carrier operation,” “multi-carrier” transmission and/orreception.

In some embodiments, the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves one or more UEs and/or that is coupled to other network nodes ornetwork elements or any radio node from where the one or more UEsreceive a signal. Examples of radio network nodes are Node B, BaseStation (BS), Multi-Standard Radio (MSR) node such as MSR BS, eNode B,network controller, Radio Network Controller (RNC), Base StationController (BSC), relay, donor node controlling relay, Base TransceiverStation (BTS), Access Point (AP), transmission points, transmissionnodes, RRU, RRH, nodes in Distributed Antenna System (DAS) etc.

Cloud Radio Access Networks (RAN) can enable the implementation ofconcepts such as Software-Defined Network (SDN) and Network FunctionVirtualization (NFV) in 5G networks. This disclosure can facilitate ageneric channel state information framework design for a 5G network.Certain embodiments of this disclosure can comprise an SDN controllerthat can control routing of traffic within the network and between thenetwork and traffic destinations. The SDN controller can be merged withthe 5G network architecture to enable service deliveries via openApplication Programming Interfaces (APIs) and move the network coretowards an all Internet Protocol (IP), cloud based, and software driventelecommunications network. The SDN controller can work with, or takethe place of Policy and Charging Rules Function (PCRF) network elementsso that policies such as quality of service and traffic management androuting can be synchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards canbe applied to 5G, also called New Radio (NR) access. 5G networks cancomprise the following: data rates of several tens of megabits persecond supported for tens of thousands of users; 1 gigabit per secondcan be offered simultaneously (or concurrently) to tens of workers onthe same office floor; several hundreds of thousands of simultaneous (orconcurrent) connections can be supported for massive sensor deployments;spectral efficiency can be enhanced compared to 4G; improved coverage;enhanced signaling efficiency; and reduced latency compared to LTE. Inmulticarrier system such as OFDM, each subcarrier can occupy bandwidth(e.g., subcarrier spacing). If the carriers use the same bandwidthspacing, then it can be considered a single numerology. However, if thecarriers occupy different bandwidth and/or spacing, then it can beconsidered a multiple numerology.

Referring now to FIG. 9, illustrated is an example block diagram of anexample mobile handset 900 operable to engage in a system architecturethat facilitates wireless communications according to one or moreembodiments described herein. Although a mobile handset is illustratedherein, it will be understood that other devices can be a mobile device,and that the mobile handset is merely illustrated to provide context forthe embodiments of the various embodiments described herein. Thefollowing discussion is intended to provide a brief, general descriptionof an example of a suitable environment in which the various embodimentscan be implemented. While the description includes a general context ofcomputer-executable instructions embodied on a machine-readable storagemedium, those skilled in the art will recognize that the innovation alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, CD ROM,digital video disk (DVD) or other optical disk storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The handset includes a processor 902 for controlling and processing allonboard operations and functions. A memory 904 interfaces to theprocessor 902 for storage of data and one or more applications 906(e.g., a video player software, user feedback component software, etc.).Other applications can include voice recognition of predetermined voicecommands that facilitate initiation of the user feedback signals. Theapplications 906 can be stored in the memory 904 and/or in a firmware908, and executed by the processor 902 from either or both the memory904 or/and the firmware 908. The firmware 908 can also store startupcode for execution in initializing the handset 900. A communicationscomponent 910 interfaces to the processor 902 to facilitatewired/wireless communication with external systems, e.g., cellularnetworks, VoIP networks, and so on. Here, the communications component910 can also include a suitable cellular transceiver 911 (e.g., a GSMtransceiver) and/or an unlicensed transceiver 913 (e.g., Wi-Fi, WiMax)for corresponding signal communications. The handset 900 can be a devicesuch as a cellular telephone, a PDA with mobile communicationscapabilities, and messaging-centric devices. The communicationscomponent 910 also facilitates communications reception from terrestrialradio networks (e.g., broadcast), digital satellite radio networks, andInternet-based radio services networks.

The handset 900 includes a display 912 for displaying text, images,video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 912 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 912 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface914 is provided in communication with the processor 902 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the handset 900, for example. Audio capabilities areprovided with an audio I/O component 916, which can include a speakerfor the output of audio signals related to, for example, indication thatthe user pressed the proper key or key combination to initiate the userfeedback signal. The audio I/O component 916 also facilitates the inputof audio signals through a microphone to record data and/or telephonyvoice data, and for inputting voice signals for telephone conversations.

The handset 900 can include a slot interface 918 for accommodating a SIC(Subscriber Identity Component) in the form factor of a card SubscriberIdentity Module (SIM) or universal SIM 920, and interfacing the SIM card920 with the processor 902. However, it is to be appreciated that theSIM card 920 can be manufactured into the handset 900, and updated bydownloading data and software.

The handset 900 can process IP data traffic through the communicationscomponent 910 to accommodate IP traffic from an IP network such as, forexample, the Internet, a corporate intranet, a home network, a personarea network, etc., through an ISP or broadband cable provider. Thus,VoIP traffic can be utilized by the handset 900 and IP-based multimediacontent can be received in either an encoded or decoded format.

A video processing component 922 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 922can aid in facilitating the generation, editing, and sharing of videoquotes. The handset 900 also includes a power source 924 in the form ofbatteries and/or an AC power subsystem, which power source 924 caninterface to an external power system or charging equipment (not shown)by a power I/O component 926.

The handset 900 can also include a video component 930 for processingvideo content received and, for recording and transmitting videocontent. For example, the video component 930 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 932 facilitates geographically locating the handset 900. Asdescribed hereinabove, this can occur when the user initiates thefeedback signal automatically or manually. A user input component 934facilitates the user initiating the quality feedback signal. The userinput component 934 can also facilitate the generation, editing andsharing of video quotes. The user input component 934 can include suchconventional input device technologies such as a keypad, keyboard,mouse, stylus pen, and/or touchscreen, for example.

Referring again to the applications 906, a hysteresis component 936facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 938 can be provided that facilitatestriggering of the hysteresis component 936 when the Wi-Fi transceiver913 detects the beacon of the access point. A SIP client 940 enables thehandset 900 to support SIP protocols and register the subscriber withthe SIP registrar server. The applications 906 can also include a client942 that provides at least the capability of discovery, play and storeof multimedia content, for example, music.

The handset 900, as indicated above related to the communicationscomponent 910, includes an indoor network radio transceiver 913 (e.g.,Wi-Fi transceiver). This function supports the indoor radio link, suchas IEEE 802.11, for the dual-mode GSM handset 900. The handset 900 canaccommodate at least satellite radio services through a handset that cancombine wireless voice and digital radio chipsets into a single handhelddevice.

Referring now to FIG. 10, illustrated is an example block diagram of anexample computer 1000 operable to engage in a system architecture thatfacilitates wireless communications according to one or more embodimentsdescribed herein. The computer 1000 can provide networking andcommunication capabilities between a wired or wireless communicationnetwork and a server (e.g., Microsoft server) and/or communicationdevice. In order to provide additional context for various aspectsthereof, FIG. 10 and the following discussion are intended to provide abrief, general description of a suitable computing environment in whichthe various aspects of the innovation can be implemented to facilitatethe establishment of a transaction between an entity and a third party.While the description above is in the general context ofcomputer-executable instructions that can run on one or more computers,those skilled in the art will recognize that the innovation also can beimplemented in combination with other program modules and/or as acombination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the various methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the innovation can also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

Communications media can embody computer-readable instructions, datastructures, program modules or other structured or unstructured data ina data signal such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference to FIG. 10, implementing various aspects described hereinwith regards to the end-user device can include a computer 1000, thecomputer 1000 including a processing unit 1004, a system memory 1006 anda system bus 1008. The system bus 1008 couples system componentsincluding, but not limited to, the system memory 1006 to the processingunit 1004. The processing unit 1004 can be any of various commerciallyavailable processors. Dual microprocessors and other multi-processorarchitectures can also be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1006includes read-only memory (ROM) 1027 and random access memory (RAM)1012. A basic input/output system (BIOS) is stored in a non-volatilememory 1027 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1000, such as during start-up. The RAM 1012 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1000 further includes an internal hard disk drive (HDD)1014 (e.g., EIDE, SATA), which internal hard disk drive 1014 can also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 1016, (e.g., to read from or write to aremovable diskette 1018) and an optical disk drive 1020, (e.g., readinga CD-ROM disk 1022 or, to read from or write to other high capacityoptical media such as the DVD). The hard disk drive 1014, magnetic diskdrive 1016 and optical disk drive 1020 can be connected to the systembus 1008 by a hard disk drive interface 1024, a magnetic disk driveinterface 1026 and an optical drive interface 1028, respectively. Theinterface 1024 for external drive implementations includes at least oneor both of Universal Serial Bus (USB) and IEEE 1394 interfacetechnologies. Other external drive connection technologies are withincontemplation of the subject innovation.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1000 the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer 1000, such aszip drives, magnetic cassettes, flash memory cards, cartridges, and thelike, can also be used in the exemplary operating environment, andfurther, that any such media can contain computer-executableinstructions for performing the methods of the disclosed innovation.

A number of program modules can be stored in the drives and RAM 1012,including an operating system 1030, one or more application programs1032, other program modules 1034 and program data 1036. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1012. It is to be appreciated that the innovation canbe implemented with various commercially available operating systems orcombinations of operating systems.

A user can enter commands and information into the computer 1000 throughone or more wired/wireless input devices, e.g., a keyboard 1038 and apointing device, such as a mouse 1040. Other input devices (not shown)can include a microphone, an IR remote control, a joystick, a game pad,a stylus pen, touchscreen, or the like. These and other input devicesare often connected to the processing unit 1004 through an input deviceinterface 1042 that is coupled to the system bus 1008, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, etc.

A monitor 1044 or other type of display device is also connected to thesystem bus 1008 through an interface, such as a video adapter 1046. Inaddition to the monitor 1044, a computer 1000 typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1000 can operate in a networked environment using logicalconnections by wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1048. The remotecomputer(s) 1048 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentdevice, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer,although, for purposes of brevity, only a memory/storage device 1050 isillustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 1052 and/or larger networks,e.g., a wide area network (WAN) 1054. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which canconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1000 isconnected to the local network 1052 through a wired and/or wirelesscommunication network interface or adapter 1056. The adapter 1056 canfacilitate wired or wireless communication to the LAN 1052, which canalso include a wireless access point disposed thereon for communicatingwith the wireless adapter 1056.

When used in a WAN networking environment, the computer 1000 can includea modem 1058, or is connected to a communications server on the WAN1054, or has other means for establishing communications over the WAN1054, such as by way of the Internet. The modem 1058, which can beinternal or external and a wired or wireless device, is connected to thesystem bus 1008 through the input device interface 1042. In a networkedenvironment, program modules depicted relative to the computer, orportions thereof, can be stored in the remote memory/storage device1050. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, in a hotel room, or a conference room at work, withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11 (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networksoperate in the unlicensed 2.4 and 5 GHz radio bands, at an 9 Mbps(802.11a) or 54 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 16BaseT wired Ethernetnetworks used in many offices.

An aspect of 5G, which differentiates from previous 4G systems, is theuse of NR. NR architecture can be designed to support multipledeployment cases for independent configuration of resources used forRACH procedures. Since the NR can provide additional services than thoseprovided by LTE, efficiencies can be generated by leveraging the prosand cons of LTE and NR to facilitate the interplay between LTE and NR,as discussed herein.

Reference throughout this specification to “one embodiment,” or “anembodiment,” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment. Thus, the appearances of the phrase “in oneembodiment,” “in one aspect,” or “in an embodiment,” in various placesthroughout this specification are not necessarily all referring to thesame embodiment. Furthermore, the particular features, structures, orcharacteristics can be combined in any suitable manner in one or moreembodiments.

As used in this disclosure, in some embodiments, the terms “component,”“system,” “interface,” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution, and/or firmware. As anexample, a component can be, but is not limited to being, a processrunning on a processor, a processor, an object, an executable, a threadof execution, computer-executable instructions, a program, and/or acomputer. By way of illustration and not limitation, both an applicationrunning on a server and the server can be a component.

One or more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components can communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software application orfirmware application executed by one or more processors, wherein theprocessor can be internal or external to the apparatus and can executeat least a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confer(s) at least in part the functionalityof the electronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system. While various components have been illustrated asseparate components, it will be appreciated that multiple components canbe implemented as a single component, or a single component can beimplemented as multiple components, without departing from exampleembodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or.” That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “mobile device equipment,” “mobile station,”“mobile,” subscriber station,” “access terminal,” “terminal,” “handset,”“communication device,” “mobile device” (and/or terms representingsimilar terminology) can refer to a wireless device utilized by asubscriber or mobile device of a wireless communication service toreceive or convey data, control, voice, video, sound, gaming orsubstantially any data-stream or signaling-stream. The foregoing termsare utilized interchangeably herein and with reference to the relateddrawings. Likewise, the terms “access point (AP),” “Base Station (BS),”BS transceiver, BS device, cell site, cell site device, “Node B (NB),”“evolved Node B (eNode B),” “home Node B (HNB)” and the like, areutilized interchangeably in the application, and refer to a wirelessnetwork component or appliance that transmits and/or receives data,control, voice, video, sound, gaming or substantially any data-stream orsignaling-stream from one or more subscriber stations. Data andsignaling streams can be packetized or frame-based flows.

Furthermore, the terms “device,” “communication device,” “mobiledevice,” “subscriber,” “customer entity,” “consumer,” “customer entity,”“entity” and the like are employed interchangeably throughout, unlesscontext warrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based on complex mathematical formalisms), which canprovide simulated vision, sound recognition and so forth.

Embodiments described herein can be exploited in substantially anywireless communication technology, comprising, but not limited to,wireless fidelity (Wi-Fi), global system for mobile communications(GSM), universal mobile telecommunications system (UMTS), worldwideinteroperability for microwave access (WiMAX), enhanced general packetradio service (enhanced GPRS), third generation partnership project(3GPP) long term evolution (LTE), third generation partnership project 2(3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA),Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacytelecommunication technologies.

The various aspects described herein can relate to New Radio (NR), whichcan be deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE), for example. It should benoted that although various aspects and embodiments have been describedherein in the context of 5G, Universal Mobile Telecommunications System(UMTS), and/or Long Term Evolution (LTE), or other next generationnetworks, the disclosed aspects are not limited to 5G, a UMTSimplementation, and/or an LTE implementation as the techniques can alsobe applied in 3G, 4G, or LTE systems. For example, aspects or featuresof the disclosed embodiments can be exploited in substantially anywireless communication technology. Such wireless communicationtechnologies can include UMTS, Code Division Multiple Access (CDMA),Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), GeneralPacket Radio Service (GPRS), Enhanced GPRS, Third Generation PartnershipProject (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2)Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), EvolvedHigh Speed Packet Access (HSPA+), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or anotherIEEE 802.XX technology. Additionally, substantially all aspectsdisclosed herein can be exploited in legacy telecommunicationtechnologies.

As used herein, “5G” can also be referred to as NR access. Accordingly,systems, methods, and/or machine-readable storage media for facilitatinglink adaptation of downlink control channel for 5G systems are desired.As used herein, one or more aspects of a 5G network can comprise, but isnot limited to, data rates of several tens of megabits per second (Mbps)supported for tens of thousands of users; at least one gigabit persecond (Gbps) to be offered simultaneously to tens of users (e.g., tensof workers on the same office floor); several hundreds of thousands ofsimultaneous connections supported for massive sensor deployments;spectral efficiency significantly enhanced compared to 4G; improvementin coverage relative to 4G; signaling efficiency enhanced compared to4G; and/or latency significantly reduced compared to LTE.

Systems, methods and/or machine-readable storage media for facilitatinga two-stage downlink control channel for 5G systems are provided herein.Legacy wireless systems such as LTE, Long-Term Evolution Advanced(LTE-A), High Speed Packet Access (HSPA) etc. use fixed modulationformat for downlink control channels. Fixed modulation format impliesthat the downlink control channel format is always encoded with a singletype of modulation (e.g., quadrature phase shift keying (QPSK)) and hasa fixed code rate. Moreover, the forward error correction (FEC) encoderuses a single, fixed mother code rate of ⅓ with rate matching. Thisdesign does not take into the account channel statistics. For example,if the channel from the BS device to the mobile device is very good, thecontrol channel cannot use this information to adjust the modulation,code rate, thereby unnecessarily allocating power on the controlchannel. Similarly, if the channel from the BS to the mobile device ispoor, then there is a probability that the mobile device might not ableto decode the information received with only the fixed modulation andcode rate. As used herein, the term “infer” or “inference” refersgenerally to the process of reasoning about, or inferring states of, thesystem, environment, user, and/or intent from a set of observations ascaptured via events and/or data. Captured data and events can includeuser data, device data, environment data, data from sensors, sensordata, application data, implicit data, explicit data, etc. Inference canbe employed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events, for example.

Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationprocedures and/or systems (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, anddata fusion engines) can be employed in connection with performingautomatic and/or inferred action in connection with the disclosedsubject matter.

In addition, the various embodiments can be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, machine-readable device, computer-readablecarrier, computer-readable media, machine-readable media,computer-readable (or machine-readable) storage/communication media. Forexample, computer-readable media can comprise, but are not limited to, amagnetic storage device, e.g., hard disk; floppy disk; magneticstrip(s); an optical disk (e.g., compact disk (CD), a digital video disc(DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g.,card, stick, key drive); and/or a virtual device that emulates a storagedevice and/or any of the above computer-readable media. Of course, thoseskilled in the art will recognize many modifications can be made to thisconfiguration without departing from the scope or spirit of the variousembodiments

The above description of illustrated embodiments of the subjectdisclosure, including what is described in the Abstract, is not intendedto be exhaustive or to limit the disclosed embodiments to the preciseforms disclosed. While specific embodiments and examples are describedherein for illustrative purposes, various modifications are possiblethat are considered within the scope of such embodiments and examples,as those skilled in the relevant art can recognize.

In this regard, while the subject matter has been described herein inconnection with various embodiments and corresponding figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

What is claimed is:
 1. A method, comprising: training, by a networkdevice, a model based on information indicative of differences betweenfirst downlink channel state information and first uplink channel stateinformation of a first mobile device, wherein the network devicecomprises a processor and a memory; and in absence of receiving seconddownlink channel state information from a second mobile device, using,by the network device, the model to determine second uplink channelstate information for the second mobile device.
 2. The method of claim1, further comprising: prior to the training of the model, populating,by the network device, a data store with the information indicative ofthe differences between the first downlink channel state information andthe first uplink channel state information.
 3. The method of claim 2,further comprising: storing, by the network device, the first uplinkchannel state information, the first downlink channel state information,and the information indicative of the differences between the firstdownlink channel state information and the first uplink channel stateinformation in the data store as a one-to-one mapping relationship. 4.The method of claim 1, further comprising: implementing, by the networkdevice, machine learning to train the model based on the informationindicative of differences between the first downlink channel stateinformation and the first uplink channel state information.
 5. Themethod of claim 1, further comprising: prior to the training of themodel, determining, by the network device, the first uplink channelstate information based on the first downlink channel state informationreceived from the first mobile device.
 6. The method of claim 5, whereinthe determining of the first uplink channel state information comprisesquantizing the first uplink channel state information based on acodebook utilized by the first mobile device to determine the firstdownlink channel state information.
 7. The method of claim 5, whereinthe determining of the first uplink channel state information comprisesestimating a spatial domain portion of a channel comprising a mainsignal receiving angle.
 8. The method of claim 5, wherein thedetermining of the first uplink channel state information comprisesestimating a spatial domain portion of a channel comprising a mainsignal transmitting angle.
 9. The method of claim 5, wherein thedetermining of the first uplink channel state information comprisescomparing the first downlink channel state information and the firstuplink channel state information during an over-the-air calibrationprocess.
 10. The method of claim 1, wherein the first downlink channelstate information comprises a reported pre-coding matrix indicator, andwherein the first uplink channel state information comprises adetermined pre-coding matrix indicator.
 11. The method of claim 10,further comprising: comparing, by the network device, the reportedpre-coding matrix indicator and the determined pre-coding matrixindicator.
 12. A system, comprising: a processor; and a memory thatstores executable instructions that, when executed by the processor,facilitate performance of operations, comprising: training a model onrespective differences between a first uplink channel state informationand a first downlink channel state information reported by a firstdevice; and based on second reported downlink channel state informationnot being received from a second device, utilizing the model todetermine second uplink channel state information for the second device.13. The system of claim 12, wherein the operations further comprise:based on the utilizing of the model to determine the second uplinkchannel state information, facilitating a mitigation of an amount ofnetwork traffic within a wireless network as compared to the seconddevice providing the second reported downlink channel state information.14. The system of claim 12, wherein the training of the model comprisesutilizing machine learning to train the model on the respectivedifferences between the first uplink channel state information and thefirst downlink channel state information.
 15. The system of claim 12,wherein the operations further comprise: determining the respectivedifferences during an over-the-air calibration process.
 16. The systemof claim 12, wherein the operations further comprise: populating a datastore that comprises the respective differences between the first uplinkchannel state information and the first downlink channel stateinformation.
 17. The system of claim 16, wherein the operations furthercomprise: facilitating a one-to-one mapping relationship between thefirst uplink channel state information and the first downlink channelstate information based on the respective differences.
 18. Amachine-readable storage medium, comprising executable instructionsthat, when executed by a processor, facilitate performance ofoperations, comprising: training a model using, as input, a differencebetween first downlink channel state information and first uplinkchannel state information received from a first user equipment; andwithout receipt of second downlink channel state information from asecond user equipment, employing the model to determine second uplinkchannel state information for the second user equipment.
 19. Themachine-readable storage medium of claim 18, wherein the operationsfurther comprise: comparing the first downlink channel state informationand the first uplink channel state information during an over-the-aircalibration process, wherein the first downlink channel stateinformation comprises a first reported pre-coding matrix indicator, andwherein the first uplink channel state information comprises a firstcomputed pre-coding matrix indicator.
 20. The machine-readable storagemedium of claim 18, wherein the operations further comprise: employingthe model to determine third uplink channel state information for athird user equipment, and wherein third downlink channel stateinformation is not received from the third user equipment.